Open Source Software and Data for Human Service Development: A Case Study on Predicting Housing Instability

TL;DR

The paper assesses how open-source data and software can support rapid, replicable analysis in resource-constrained human services by forecasting residential non-payment eviction filings in Bronx County during COVID-19. Using open data sources (OCA eviction filings, ACS, DeepMaps) and open-source R tools, the authors implement multilevel models and exponential smoothing to forecast housing instability and compare results to actual outcomes. Findings suggest open data/tools enable timely planning and targeted interventions, but granularity and data provenance limit accuracy, often leading to overestimation of need. The work demonstrates replication with open data/code and offers practical lessons on leveraging open resources for local, low-resource communities in the face of public policy shocks.

Abstract

Open-source data and tools are lauded as essential for replicable and usable social science, though little is known about their use in resource constrained human service provision. This paper examines the challenges and opportunities of open-source tools and data in human service development by using both to forecast failure to pay eviction filings in Bronx County, NY. We use zip code level data from the Housing Data Coalition, the American Community Survey 5-year estimates, and DeepMaps Model of the Labor Force to forecast rates through July 2021. We employ multilevel (MLM) and exponential smoothing (ETS) models using the R project for Statistical Computing, an oft used open-source statistical software. We compare our results to what happened during the same period, to illustrate the efficacy of the open-source tools and techniques employed. We argue open-source data and software may facilitate rapid analysis of public data - a much-needed ability in human service intervention development under increasingly constrained resources - but find public data are limited by the information they reliably capture, limiting their utility by a non-trivial margin of error. The manuscript concludes by considering lessons for human service organizations with limited analytical resources and a vested interest in low-resourced communities.